An important question for both signal processing and auditory science is to understand which features of a sound carry the most important information for the listener. Here we approach the issue by introducing the idea of "auditory sketches": sparse representations of sounds, severely impoverished compared to the original, which nevertheless afford good performance on a given perceptual task. Starting from biologically-grounded representations (auditory models), a sketch is obtained by reconstructing a highly under-sampled selection of elementary atoms. Then, the sketch is evaluated with a psychophysical experiment involving human listeners. The process can be repeated iteratively. As a proof of concept, we present data for an emotion recognition task with short non-verbal sounds. We investigate 1/ the type of auditory representation that can be used for sketches 2/ the selection procedure to sparsify such representations 3/ the smallest number of atoms that can be kept 4/ the robustness to noise. Results indicate that it is possible to produce recognizable sketches with a very small number of atoms per second. Furthermore, at least in our experimental setup, a simple and fast under-sampling method based on selecting local maxima of the representation seems to perform as well or better than a more traditional algorithm aimed at minimizing the reconstruction error. Thus, auditory sketches may be a useful tool for choosing sparse dictionaries, and also for identifying the minimal set of features required in a specific perceptual task.
展开▼
